Generative AI in Managerial Decision-Making: Redefining Boundaries through Ambiguity Resolution and Sycophancy Analysis

TL;DR

This paper investigates how generative AI can assist managerial decision-making by detecting and resolving ambiguities, analyzing sycophantic tendencies, and improving response quality in complex business scenarios through a novel taxonomy and human-in-the-loop evaluation.

Contribution

It introduces a four-dimensional ambiguity taxonomy, evaluates models' ambiguity detection and resolution capabilities, and analyzes sycophantic behavior, advancing understanding of GAI's role in managerial contexts.

Findings

Models excel at detecting contradictions and ambiguities.

Ambiguity resolution improves decision response quality.

Sycophantic behavior varies by model architecture.

Abstract

Generative artificial intelligence is increasingly being integrated into complex business workflows, fundamentally shifting the boundaries of managerial decision-making. However, the reliability of its strategic advice in ambiguous business contexts remains a critical knowledge gap. This study addresses this by comparing various models on ambiguity detection, evaluating how a systematic resolution process enhances response quality, and investigating their sycophantic behavior when presented with flawed directives. Using a novel four-dimensional business ambiguity taxonomy, we conducted a human-in-the-loop experiment across strategic, tactical, and operational scenarios. The resulting decisions were assessed with an "LLM-as-a-judge" framework on criteria including agreement, actionability, justification quality, and constraint adherence. Results reveal distinct performance capabilities. While models excel in detecting internal contradictions and contextual ambiguities, they struggle with structural linguistic nuances. Ambiguity resolution consistently increased response quality across all decision types, while sycophantic behavior analysis revealed distinct patterns depending on the model architecture. This study contributes to the bounded rationality literature by positioning GAI as a cognitive scaffold that can detect and resolve ambiguities managers might overlook, but whose own artificial limitations necessitate human management to ensure its reliability as a strategic partner.